Director, Clinical AI Delivery Operations

• Define, implement, and maintain team-wide standards, SOPs, templates, and checklists for data and model intake, curation, dataset release, documentation, and handover.
• Own the team's approach to data and model governance and quality. This covers dataset versioning, lineage and traceability, fit-for-purpose criteria, and quality control.
• Serve as the point of contact for governance, compliance, and quality questions. Keep the team aligned with internal policies and external expectations as they evolve.
• Establish metrics for delivery performance, data and model quality, and compliance, and report on them to team leadership.
• Support audit and inspection readiness for the team's data and AI workflows.

• Coordinate with enterprise data owners, clinical data management, IT, and external collaborators to access and transfer multimodal data, models, and associated metadata.
• Ensure datasets and models meet defined formats, metadata standards, and project requirements for each modality.
• Translate scientific and model development requirements into structured dataset preparation tasks.
• Verify the completeness, accuracy, and quality of datasets in line with SOPs.
• Maintain consistency, traceability, and usability of datasets across projects.

• Run and coordinate the operational workflows that take clinical AI work from research to evidence-backed output. This includes data pooling, subsetting and splitting, annotation consolidation, and reproducible data preparation.
• Support handover of validated datasets, models, and outputs to clinical, regulatory, engineering, and IT partners.
• Manage storage and release of results in approved data environments.

• Ensure datasets and workflows comply with internal policies and governance frameworks. Examples are data privacy, human biological sample governance, and data access approvals (e.g., iDAP).
• Initiate and coordinate approvals with the Data Enablement, Quality, IT, and Compliance teams.
• Build in the data integrity and audit-readiness expected for regulated work.
• Author and maintain documentation such as data collection plans, data and model lineage records, analysis plans, and reports.
• Manage QC checks, deviations, and issue resolution.

• Work closely with AI scientists, ML engineers, data scientists, program managers, clinical and regulatory business partners, IT and data teams.
• Communicate clearly about data and model readiness, limitations, and uncertainties to support decisions.

• Identify and implement improvements to workflows, tooling, and processes so that delivery scales as the team grows.
• Stay informed about emerging data and model management practices, clinical and multimodal data and model standards, and AI governance expectations.

• Bachelor's or Master's degree in data science, bioinformatics, health or biomedical informatics, or a related field.
• Experience in clinical data management, life sciences, or healthcare is highly desirable.

• 5 years of experience managing complex scientific or clinical datasets, including multimodal data.
• Experience defining or owning data and/or model management processes, standards, or governance frameworks, ideally in a regulated environment.
• Fluency with FAIR data principles and their application in life sciences.
• Working knowledge of cloud-based data environments, modern data platforms, and dataset management workflows.
• Understanding of AI/ML data requirements for model development, validation, and monitoring.
• Experience with dataset versioning, reproducible data preparation, and quality control.
• Working knowledge of Python or a similar scripting language for data preparation, automation, and QC.
• Familiarity with data governance, compliance processes, and controlled handling of protected data.
• Proven ability to coordinate delivery across multiple stakeholders and workstreams.
• Familiarity with generative AI or agentic systems in clinical or regulatory settings.
• Experience with MLOps, data pipelines, or model lifecycle documentation.

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• Knowledge of clinical and regulatory data standards (e.g., CDISC SDTM/ADaM, eCTD, DICOM) and of GxP computerized system validation.
• Experience supporting audits or inspections.
• Working knowledge of Atlassian tools (JIRA, Confluence).

• Excellent interpersonal, verbal, and written communication skills in an international matrix environment.
• Ability to influence without authority and to gain adoption of standards across scientific and engineering teams.
• Strong execution mindset, with ownership and accountability for high-quality delivery.
• Highly structured and detail-oriented, with strong follow-through.
• Pragmatic, fit-for-purpose problem solving, especially when diagnosing data quality or workflow issues.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world. The annual base pay for this position ranges from $186,014.40 - $279,021.60 USD Annual [80% - 120% ]. Our positions offer eligibility for various incentives - an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.